Comments for MEDB 5510, Week 06

Topics to be covered

  • What you will learn, part 1
    • Secondary data analysis
    • Centers for Disease Control and Prevention
    • Bureau of the Census
    • Cohort designs
    • Case-control designs
    • Cross-sectional designs
    • Historical control designs
    • Establishing causation in an observational design
  • What you will learn, part 2
    • Qualitative data, phenomenology
    • Grounded theory
    • Ethnography
    • The case sudy
    • Narrative
    • Community-Based Participatory Research

Learning objectives

  1. To distinguish different types of quantitative non-experimental approaches

  2. To discuss strengths and weaknesses of qualitative research

What is secondary data analysis?

  • Primary: data that you both collect and analyze.
  • Secondary: you analyze someone else’s data
    • “Secondary analysis of existing data” (Cheng 2014)
  • Private versus government sources
  • Ancillary studies

What can you analyze for an already analyzed data set?

  • Secondary data analysis is like eating left-over food.
  • “Think different”
    • Variables that are analyzed
    • Relationships that are explored
    • Different subsets of cases
    • Different analysis techniques

Combining data sets

  • Third “V” in volume, velocity, and variety
  • Not the same as systematic overview, think “mash-up”
  • Examples
    • Black box warnings and prescription use
    • Staffing levels and patient complaints
    • Pediatric asthma visits and housing survey

Disadvantages of secondary analysis

  • Missing data
    • Variables not collected
    • Specific details not collected
  • Wrong data
    • Wrong time
    • Wrong measures
  • Stale data
  • Unable to fix obvious errors

Advantages of secondary analysis

  • Time
  • Money
  • Support

Two types of secondary data sets

  • Individual
    • More analysis options
    • Privacy concerns
  • Aggregate
    • Mixture of apples and oranges
    • Problems with the ecologic fallacy

Getting started with secondary data analysis

  • Start with an existing database
  • Get familiar with the data
    • What variables are there?
    • What variables are not there?
    • How are the variables coded?
    • Identify potential pairs for associational studies
  • Make sure you are answering an interesting question

Alternative strategy for secondary analysis

  • Start with a research hypothesis
    • List the inclusion/exclusion criteria
    • List the relevant variables
  • Search databases to see if they are a good fit.
    • Do they have the right patients?
    • Where is your control group?
    • Do they have the right variables?
    • Are there restrictions on using the database?

Get help from the literature

  • How has database been used previously?
  • What has already been answered?
  • What was their analysis plan?
  • Understand the limitations.

Break #1

  • What you have learned
    • Secondary data analysis
  • What’s coming next
    • Centers for Disease Control and Prevention

“The Nation’s Health Protection Agency”

Nationwide cross sectional surveys

National Health Interview Survey

National Health and Nutrition Examination Survey

Behavioral Risk Factor Surveillance System

National Death Index

Influenza Hospitalization Surveillance Network

FluView

Foodborne Diseases Active Surveillance Network

Active Bacterial Core Surveillance

Summary

  • CDC
    • NHIS
    • NHANES
    • BRFSS
    • NDI
    • FluSurv-NET
    • FluView
    • FoodNet

Break #2

  • What you have learned
    • Centers for Disease Control and Prevention
  • What’s coming next
    • Bureau of the Census

United States Bureau of the Census

Article I, Section II of the U.S. Constitution

Integrated Public Use Microdata Series International Partnership

American Community Survey

Master Address File

Census blocks

Topologically Integrated Geographic Encoding and Referencing System

American Housing Survey

Current Population Survey

Consumer Expenditure Survey

National Crime Victimization Survey

Summary

  • Census
    • ACS
    • MAF
    • Census blocks
    • TIGER files
    • AHS
    • CPS
    • CES
    • NCVS

Break #3

  • What you have learned
    • Bureau of the Census
  • What’s coming next
    • Cohort designs

Research Approaches

Why not randomize everything?

  • Can’t randomize because…
    • Impossible
    • Impractical
    • Unethical
    • Strong patient preference
    • Retrospective studies

Departures from Gliner et al

  • “Observational” instead of “non-experimental”
  • Categorize by sample selection
    • Cohort
    • Cross-sectional
    • Case-control
    • Historical control

What is a cohort design?

  • Cohort defined by exposure
  • Compared to unexposed controls
  • Prospective or retrospective

Disadvantages of cohort designs

  • Disadvantages
    • Difficult for rare diseases, long latency
    • Selection bias, confounding
  • Advantages
    • Can examine multiple outcomes
    • Easy to explain
    • Well defined comparison group
    • Adaptable to longitudinal analysis

Advantages of cohort designs

  • Advantages
    • Can examine multiple outcomes
    • Easy to explain
    • Well defined comparison group
    • Adaptable to longitudinal analysis

Break #4

  • What you have learned
    • Cohort designs
  • What’s coming next
    • Case-control designs

What is a case-control design?

  • Cases defined by outcome
  • Comparison to controls without the outcome
  • Always retrospective

Disadvantages of the case-control design

  • Disadvantages
    • Counter-intuitive appearance
    • Difficulty in identifying good controls
    • Confounding

Advantages of the case-control design

  • Advantages
    • Can examine multiple risk factors
    • Efficient for rare diseases
    • Great starting point for mysterious outcomes

Selecting controls in a case-control design

  • Selecting controls
    • Hospital/clinic based
    • Community
    • Relative/friends
    • Within the same cohort

Break #5

  • What you have learned
    • Case-control designs
  • What’s coming next
    • Cross-sectional designs

What is a cross-sectional design?

  • Single group
    • No selection by exposure
    • No selection by outcome
  • Can be prospective or retrospective

Disadvantages of cross-sectional designs

  • Disadvantages
    • Confusion about temporal ordering
    • Selection bias, confounding

Advantages of cross-sectional designs

  • Advantages
    • Examine multiple risk factors, multiple outcomes
    • Realistic setting

Break #6

  • What you have learned
    • Cross-sectional designs
  • What’s coming next
    • Historical control designs

What are historical control designs

  • Controls separated by time
    • Similar to the single group, pre-post measurement
  • Sometimes separated by space
    • Similar to the two group, post measurement only
  • Often separated by both space and time

Disadvantages and advantages of historical control designs

  • Disadvantages
    • Confounding
  • Advantages
    • Cheap and easy
  • Settings with 100% morbidity or mortality

Break #7

  • What you have learned
    • Historical control designs
  • What’s coming next
    • Establishing causation in an observational design

Causation and observational designs

  • Observational designs CAN establish a causal relationship
    • Just requires more work
    • Control for confounding
    • Bring in external evidence
  • Hill’s nine criteria (strength, consistency, specificity, temporality, biological gradient, plausibility, experiment, analogy)

Hill’s criteria, strength

  • Strength
    • Large effects can only be overturned by large confounders.
    • Weak effects can still be real

Hill’s criteria, consistency

  • Consistency
    • Replication across DIFFERENT study types

Hill’s criteria, specificity

  • Specificity
    • Multiple cures or common bias?
    • Deliberate inclusion of negative outcomes
    • Exceptions: aspirin, smoking

Hill’s criteria, temporality

  • Temporality
    • A has to precede B to be a cause
    • Advantage of prospective studies
    • Difficult for long latency diseases

Hill’s criteria, biological gradient

  • Biological gradient
    • Dose response relationship
    • Rule out some, but not all confounders
    • Hormesis and other patterns

Hill’s criteria, plausibility

  • Plausibility
    • Biological mechanism
    • Dependent on current state of knowledge

Hill’s criteria, coherence

  • Coherence
    • Follows natural history
    • Consistent with biology

Hill’s criteria, experiment

  • Experiment

Hill’s criteria, analogy

  • Analogy
    • Similar to coherence?

Break #8

  • What you have learned
    • Establishing causation in an observational design
  • What’s coming next
    • Qualitative data, phenomenology

Five approaches to qualitative research

  • Qualitative – 5 main approaches
    • Phenomenology
    • Grounded theory
    • Ethnography
    • Case study
    • Narrative
  • Relies on a constructivist philosophy
    • Rejection of the single reality of positivism
    • Research protocol adapts as new information emerges

What is phenomenology?

  • Phenomenom: “an observable fact or event” (Merriam-Webster)
    • What meaning do people place on this fact or event?
    • How do people construct their own reality around certain events

An example of phenomenology, 1 of 2

An example of phenomenology, 2 of 2

Image of Pallesen 2019 abstract

Break #9

  • What you have learned
    • Qualitative data, phenomenology
  • What’s coming next
    • Grounded theory

What is grounded theory?

  • Generate theory from data collected from participants
    • Inferences firmly “grounded” in the data
    • Prior theoretical expectations avoided
    • Sampling proceeds parallel to data collection & analysis
    • Research maintains skepticism, seeks disconfirming examples

An example of grounded theory, 1 of 2

An example of grounded theory, 1 of 2

Break #10

  • What you have learned
    • Grounded theory
  • What’s coming next
    • Ethnography

What is ethnography?

  • Study of individuals who share the same culture
    • Methods developed in Sociology/Anthropology
    • Strong emphasis on observation

An example of ethnography, 1 of 2

Image of Khoei 2018 article

An example of ethnography, 2 of 2

Image of Khoei 2018 abstract

Break #11

  • What you have learned
    • Ethnography
  • What’s coming next
    • The case sudy

What is a case study?

  • In depth examination of a case or series of cases
    • Not the same as a case report
    • Viewed from a variety of lenses
  • Narrow definition
  • Emphasis on unusualness

An example of a case study, 1 of 2

Image of Phehlukwayo 2018 article

An example of a case study, 2 of 2

Image of Phehlukwayo 2018 abstract

Break #12

  • What you have learned
    • The case sudy
  • What’s coming next
    • Narrative

What is a narrative?

  • Narrative is a written account of an event
    • Story developed from a variety of perspectives
    • Use interviews, documents, artifacts

An example of a narrative, 1 of 2

Image of Nakanishi 2019 article

An example of a narrative, 2 of 2

Image of Nakanishi 2018 abstract

Break #13

  • What you have learned
    • Narrative
  • What’s coming next
    • Community-Based Participatory Research

Problems with traditional research

  • “Researchers are like mosquitoes; they suck your blood and leave” from Cochran 2008.
  • Harms of research
    • Stigmatization
    • Condescension
    • Reinforcement of stereotypes
    • Cultural insensitivity
    • Failure to respect community standards
    • Abandonment when the research is done
    • Deception about true research purpose

What is Community Based Participatory Research (CBPR)?

  • Designed in collaboration with the people being researched
  • Also known as Participatory Action Research (PAR)
  • Not restricted to any research methodology

Disadvantages of CBPR

  • Designed in collaboration with the people being researched
  • Also known as Participatory Action Research (PAR)
  • Not restricted to any particular research methodology

Advantages of CBPR

  • Advantages to you, the researcher
    • Precisely targeted intervention
    • Improved participation rate
    • Greater generalizability
  • Advantages to the community
    • Sustainability
    • Greater community capacity

Getting started with CBPR

  • Pick a problem
    • Simple
    • Important
    • Needed
  • Ideal settings
    • Health disparities
    • Complex interventions
  • Identify resources
  • Keep an open mind
  • Be willing to compromise

Emphasize your strengths

  • Things you know that they don’t
    • Existing research evidence
    • Ability to diagnose
    • Natural course and history of disease
    • How to measure
    • Research standards

Understand the strengths of the community members

  • Things that they know that you don’t
    • Specific needs of their community
    • Where to find research volunteers
    • What they will and will not tolerate
    • How to tweak an intervention
    • Pragmatic advice

Summary

  • What you have learned, part 1
    • Secondary data analysis
    • Centers for Disease Control and Prevention
    • Bureau of the Census
    • Cohort designs
    • Case-control designs
    • Cross-sectional designs
    • Historical control designs
    • Establishing causation in an observational design
  • What you have learned, part 2
    • Qualitative data, phenomenology
    • Grounded theory
    • Ethnography
    • The case sudy
    • Narrative
    • Community-Based Participatory Research